from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-18 14:09:12.295965
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 18, Dec, 2020
Time: 14:09:15
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.7199
Nobs: 144.000 HQIC: -44.8218
Log likelihood: 1532.53 FPE: 1.61228e-20
AIC: -45.5761 Det(Omega_mle): 8.81081e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.452554 0.173214 2.613 0.009
L1.Burgenland 0.146926 0.084105 1.747 0.081
L1.Kärnten -0.235847 0.067852 -3.476 0.001
L1.Niederösterreich 0.113641 0.201323 0.564 0.572
L1.Oberösterreich 0.245429 0.168662 1.455 0.146
L1.Salzburg 0.177436 0.086821 2.044 0.041
L1.Steiermark 0.087078 0.121708 0.715 0.474
L1.Tirol 0.146855 0.080136 1.833 0.067
L1.Vorarlberg 0.007951 0.077907 0.102 0.919
L1.Wien -0.124736 0.163481 -0.763 0.445
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.570484 0.226541 2.518 0.012
L1.Burgenland 0.014031 0.109999 0.128 0.899
L1.Kärnten 0.361561 0.088741 4.074 0.000
L1.Niederösterreich 0.118052 0.263305 0.448 0.654
L1.Oberösterreich -0.214558 0.220588 -0.973 0.331
L1.Salzburg 0.194752 0.113550 1.715 0.086
L1.Steiermark 0.238162 0.159179 1.496 0.135
L1.Tirol 0.145712 0.104807 1.390 0.164
L1.Vorarlberg 0.187955 0.101893 1.845 0.065
L1.Wien -0.596154 0.213812 -2.788 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.304272 0.075213 4.045 0.000
L1.Burgenland 0.105458 0.036520 2.888 0.004
L1.Kärnten -0.026079 0.029463 -0.885 0.376
L1.Niederösterreich 0.072985 0.087419 0.835 0.404
L1.Oberösterreich 0.286527 0.073236 3.912 0.000
L1.Salzburg -0.000853 0.037699 -0.023 0.982
L1.Steiermark -0.029539 0.052848 -0.559 0.576
L1.Tirol 0.090337 0.034797 2.596 0.009
L1.Vorarlberg 0.131677 0.033829 3.892 0.000
L1.Wien 0.072541 0.070987 1.022 0.307
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.198220 0.086383 2.295 0.022
L1.Burgenland -0.003645 0.041944 -0.087 0.931
L1.Kärnten 0.020065 0.033838 0.593 0.553
L1.Niederösterreich 0.009532 0.100401 0.095 0.924
L1.Oberösterreich 0.403622 0.084112 4.799 0.000
L1.Salzburg 0.096106 0.043298 2.220 0.026
L1.Steiermark 0.194245 0.060697 3.200 0.001
L1.Tirol 0.032425 0.039964 0.811 0.417
L1.Vorarlberg 0.102961 0.038853 2.650 0.008
L1.Wien -0.054483 0.081529 -0.668 0.504
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.631920 0.182306 3.466 0.001
L1.Burgenland 0.077692 0.088520 0.878 0.380
L1.Kärnten 0.002573 0.071413 0.036 0.971
L1.Niederösterreich -0.078011 0.211891 -0.368 0.713
L1.Oberösterreich 0.130468 0.177515 0.735 0.462
L1.Salzburg 0.042470 0.091378 0.465 0.642
L1.Steiermark 0.120216 0.128097 0.938 0.348
L1.Tirol 0.220737 0.084342 2.617 0.009
L1.Vorarlberg 0.017188 0.081997 0.210 0.834
L1.Wien -0.146300 0.172063 -0.850 0.395
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.177678 0.126802 1.401 0.161
L1.Burgenland -0.031072 0.061570 -0.505 0.614
L1.Kärnten -0.014870 0.049671 -0.299 0.765
L1.Niederösterreich 0.151259 0.147380 1.026 0.305
L1.Oberösterreich 0.402516 0.123469 3.260 0.001
L1.Salzburg -0.024075 0.063558 -0.379 0.705
L1.Steiermark -0.041636 0.089097 -0.467 0.640
L1.Tirol 0.188988 0.058664 3.222 0.001
L1.Vorarlberg 0.037136 0.057032 0.651 0.515
L1.Wien 0.164093 0.119677 1.371 0.170
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.203233 0.159091 1.277 0.201
L1.Burgenland 0.084291 0.077248 1.091 0.275
L1.Kärnten -0.045284 0.062319 -0.727 0.467
L1.Niederösterreich -0.044910 0.184908 -0.243 0.808
L1.Oberösterreich -0.122969 0.154910 -0.794 0.427
L1.Salzburg 0.010744 0.079742 0.135 0.893
L1.Steiermark 0.389532 0.111785 3.485 0.000
L1.Tirol 0.519536 0.073602 7.059 0.000
L1.Vorarlberg 0.225272 0.071555 3.148 0.002
L1.Wien -0.217514 0.150152 -1.449 0.147
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.090609 0.184156 0.492 0.623
L1.Burgenland 0.032228 0.089418 0.360 0.719
L1.Kärnten -0.115165 0.072138 -1.596 0.110
L1.Niederösterreich 0.178752 0.214041 0.835 0.404
L1.Oberösterreich 0.018222 0.179316 0.102 0.919
L1.Salzburg 0.225640 0.092305 2.444 0.015
L1.Steiermark 0.153158 0.129397 1.184 0.237
L1.Tirol 0.087701 0.085198 1.029 0.303
L1.Vorarlberg 0.038636 0.082829 0.466 0.641
L1.Wien 0.300450 0.173808 1.729 0.084
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.586538 0.102269 5.735 0.000
L1.Burgenland -0.014061 0.049658 -0.283 0.777
L1.Kärnten -0.001213 0.040061 -0.030 0.976
L1.Niederösterreich -0.036490 0.118866 -0.307 0.759
L1.Oberösterreich 0.279072 0.099582 2.802 0.005
L1.Salzburg 0.009148 0.051261 0.178 0.858
L1.Steiermark 0.009663 0.071859 0.134 0.893
L1.Tirol 0.077789 0.047314 1.644 0.100
L1.Vorarlberg 0.180794 0.045998 3.930 0.000
L1.Wien -0.085461 0.096523 -0.885 0.376
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.133143 -0.017000 0.189717 0.242494 0.033008 0.084582 -0.123733 0.146838
Kärnten 0.133143 1.000000 -0.025067 0.178871 0.123226 -0.159476 0.169139 0.021809 0.296003
Niederösterreich -0.017000 -0.025067 1.000000 0.254504 0.060188 0.199389 0.088874 0.024608 0.351772
Oberösterreich 0.189717 0.178871 0.254504 1.000000 0.268743 0.276476 0.081747 0.051952 0.069354
Salzburg 0.242494 0.123226 0.060188 0.268743 1.000000 0.140371 0.060081 0.071330 -0.040844
Steiermark 0.033008 -0.159476 0.199389 0.276476 0.140371 1.000000 0.096885 0.067232 -0.163636
Tirol 0.084582 0.169139 0.088874 0.081747 0.060081 0.096885 1.000000 0.129436 0.112284
Vorarlberg -0.123733 0.021809 0.024608 0.051952 0.071330 0.067232 0.129436 1.000000 0.074756
Wien 0.146838 0.296003 0.351772 0.069354 -0.040844 -0.163636 0.112284 0.074756 1.000000